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Cited by 241Technology Adoption and User BehaviourTechnology-Enhanced Education StudiesOnline and Blended Learning

The DOI matches its Crossref record; 130 of the 130 references it cites were found in a registry or an index; none of the 37 sources its claims cite is checked for support yet.

Acceptance of e-learning in higher education: The role of task-technology fit with the information systems success model

Open access
Alyoussef, Ibrahim Youssef·2023·Heliyon
DOI 10.1016/j.heliyon.2023.e13751PMC PMC9938001
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Abstract

The COVID-19 global epidemic has compelled higher education institutions to reconsider their teaching methods. Because of this public health emergency, universities in higher education have adopted e-learning techniques as a solution to face-to-face education. Thus, e-learning has emerged as a critical technology in education at higher education institutions. Nonetheless, the effectiveness of e-learning systems is largely dependent on students' adoption of such systems. The study aims to evaluate the usefulness of task-technology fit (TTF) with the information system success model (ISSM) in perceiving students' adoption of e-learning with the goal of encouraging them to adopt e-learning in the context of higher education. The study employed a quantitative approach, and a theoretical model was evaluated with proposed hypotheses to find the relationships between the constructs. A questionnaire based on TTF and ISSM was distributed among the students, and 260 valid responses were received using a sample random sampling technique. Data was analyzed with the help of SPSS and Partial Least Squares-Structural Equation Modeling (PLS-SEM). After analyzing the data, it was found that perceived ease of use, perceived usefulness, system use, and task technology fit of e-learning are positively and significantly influenced by system quality, information quality, perceived enjoyment, technology characteristics, and task characteristics. The results of TTF and ISSM on system use show a positive effect on e-learning benefits in educational institutions, with all male and female students completely satisfied with the use of e-learning systems. As a result, we advise students to use e-learning systems for educational purposes and should have motivated them to do so through lecturers at higher-level educational institutions.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This study integrates task-technology fit (TTF) with the information systems success model (ISSM) to explain undergraduate students’ e-learning acceptance at King Faisal University. Using PLS-SEM on 260 valid survey responses, it finds all 18 hypothesized paths supported, with TTF showing the strongest effect on e-learning benefit.

Key findings

  • All 18 hypotheses were accepted. TTF had the strongest positive relationship with e-learning benefit (β = 0.643, t = 16.457, p = 0.000), followed by PEOU → TTF (β = 0.441, t = 8.580) and PU → system use (β = 0.371, t = 4.706).
  • Information quality, system quality, perceived enjoyment, technology characteristics, and task characteristics positively predicted PEOU and PU; for example, IQ → PEOU β = 0.284, t = 4.409; IQ → PU β = 0.256, t = 4.238; PE → PEOU β = 0.232, t = 3.562; PE → PU β = 0.241, t = 3.619.
  • System use positively predicted e-learning benefit (β = 0.310, t = 7.260), and PEOU, PU, and system use also predicted TTF (β = 0.441, 0.339, and 0.140, respectively).
  • The measurement model reported Cronbach’s alpha and composite reliability above 0.7 and AVE above 0.5 for the constructs.

Methods

  • Quantitative survey design testing an integrated TTF + ISSM model with perceived enjoyment as an external factor; a 5-point Likert questionnaire (described as 50 questions in the conclusion) was administered physically and online to King Faisal University students.
  • 275 questionnaires were distributed, yielding 260 valid responses after excluding 15 incomplete/unanswered questionnaires, a 94.5% response rate; participants were 187 male (71.9%) and 73 female (28.1%), ages 18–46, across management, engineering, science and technology, social science, and other fields.
  • Data were analyzed with SPSS and SmartPLS 3.3.3 using PLS-SEM; the measurement model assessed reliability and validity, and the structural model used bootstrapping with 5000 resamples.

Limitations

  • Stated: data came from one university, which may affect generalizability; the scope was restricted to two possibilities regarding prior e-learning expertise; possible extrinsic variables related to system use and TTF were not considered; future research should include more participants with varied majors.
  • Evident: the sampling description is inconsistent—the abstract says random sampling, while the methodology also says convenience sampling—and the sample is heavily male (71.9%), which may limit representativeness.
  • Evident: the study uses a self-report questionnaire collected at the start of the 2020–2021 academic year, so causal claims are not established by the design.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.heliyon.2023.e13751

Cites

130 of 130 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 37 cited sources checked · 0 supported · 0 flagged

Show all 48 claims

  • It may state two separate claims as one. 11
  • Probably not a substantial claim. 26
  • The quote only partly states this claim. 1
31 of 31

  • The paper relies on this without support.

    The effectiveness of e-learning systems is largely dependent on students' adoption of such systems.

    “Nonetheless, the effectiveness of e-learning systems is largely dependent on students' adoption of such systems.”

  • The paper relies on this without support.

    There is a scarcity of research on university students' intentions to adopt e-learning during the global epidemic that uses TTF and ISSM with perceived enjoyment as an external factor, especially in developing countries such as education.

    “Moreover, there is a scarcity of research on university students' intentions to adopt e-learning during the global epidemic that uses the TTF and ISSM models with perceived enjoyment as an external factor of the model, especially in developing countries such as education, where e-learning was not widely used at universities prior to the pandemic.”

  • The paper relies on this without support.

    E-learning was not widely used at universities prior to the pandemic in developing countries such as education.

    “where e-learning was not widely used at universities prior to the pandemic.”

  • The paper relies on this without support.

    TTF has been the subject of research in a number of contexts, but e-learning has received relatively less attention.

    “TTF has been the subject of research in a number of contexts, but e-learning has received relatively less attention.”

  • The paper relies on this without support.

    Up to this point, it has not been determined if or how well a solid task-technology fit will affect a user’s adoption of e-learning.

    “Up to this point, it hasn’t been determined if or how well a solid task-technology fit will affect a user’s adoption of e-learning.”

  • The paper relies on this without support.

    The TTF model does not take into account social dynamics in the context of e-learning.

    “The TTF model does not take into account social dynamics in the context of e-learning, which would limit its potential to forecast the use of social networking technology.”

  • The paper relies on this without support.

    The sample of 260 students from King Faisal University is representative of students in higher education more broadly.

    “260 randomly chosen students from King Faisal University who were enrolled in bachelors, masters, and PhD programs, both domestically and internationally, were the source of the data.”

  • The paper relies on this without support.

    The findings can be generalized to higher education in Saudi Arabia.

    “The five e-learning benefit dimensions information quality, system quality, perceived enjoyment, technology characteristics, and task characteristics were used as potential constructs that might affect how easy and useful an e-learning system is to use in Saudi Arabia as well as how well it fits into the system as a whole and how well it fits into the task-technology fit.”

  • The paper relies on this without support.

    The respondents answered the questionnaire honestly and accurately.

    “The questionnaire was distributed physically, and respondents were asked to return it once completed.”

  • The paper relies on this without support.

    This is the first study to look into the acceptance of eLearning benefits among undergraduate students during the COVID-19 epidemic.

    “Furthermore, this is the first study to look into the acceptance of eLearning benefits among undergraduate students during the COVID-19 epidemic.”

  • The paper relies on this without support.

    e-learning was widely used as a teaching medium during the COVID-19 epidemic, particularly among students in higher education.

    “In conclusion, e-learning was widely used as a teaching medium during the COVID-19 epidemic, particularly among students in higher education.”

  • The paper relies on this without support.

    A few studies have examined the use of e-learning during pandemics like COVID-19.

    “However, a few studies have examined the use of e-learning during pandemics like COVID-19.”
12 of 12

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Sampling method is described as both convenience and random

    Major

    The methods section says participants were chosen using convenience sampling, but also describes the students supplying the data as randomly chosen.

  • Ethics approval date follows the stated study period

    Major

    The paper says approval was required before the work was carried out, but identifies an October 2022 ethics clearance while stating that the research was conducted at the start of the 2020-2021 academic year.

  • H4 p-value differs from Table 6

    Minor

    The discussion assigns a p-value of 0.001 to both H3 and H4, but Table 6 gives 0.000 for H4.

  • H14 path result differs between the text and Table 6

    Minor

    The text reports a coefficient of 0.441 and t-value of 8.580 for H14, while Table 6 reports 0.371 and 4.706 for the same perceived-usefulness-to-system-use path.

4 of 4

Citations

Cites

Flagged first, then by citations

Show all 125 references

Research works printed but not linked: 2

  1. Klopping, 2004, Extending the technology acceptance model and the task-technology fit model to consumer e-consumer, Inf. Technol. Learn. Perform J., 22, 35
  2. Sathishkumar, 2020, E-Learning during lockdown of covid-19 pandemic: a global perspective, Int. J. Control Autom., 13, 1088

Cited by

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Metadata

Authors
Alyoussef, I. Y.
Affiliations
King Faisal University (SA)
Corresponding author
Alyoussef, Ibrahim Youssef
ORCID
Alyoussef, Ibrahim Youssef
Venue
Heliyon
Publisher
Elsevier BV
Published
2023-03
Volume
9
Issue
3
Pages
e13751
ISSN
2405-8440 (ISSN-L)
DOI
10.1016/j.heliyon.2023.e13751
PMC
PMC9938001
OpenAlex
W4321254258
Cited by
181
References
125
Citations by year
2026: 45 · 2025: 53 · 2024: 59 · 2023: 23
FWCI
46.23
Type
journal article
Language
en
Open access
Licence
Locations
  • Heliyon (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Deanship of Scientific Research, King Saud University
  • King Faisal University (2662)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Education › Technology-Enhanced Education Studies
  • Social Sciences › Education › Online and Blended Learning
Keywords
e-learning adoption, task-technology fit, DeLone and McLean model, university students, perceived ease of use, perceived usefulness, system quality, information quality, perceived enjoyment, technology characteristics, task characteristics, COVID-19 remote teaching, student acceptance of technology

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